Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems.
Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems.
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DOI：
10.1186/s12859-014-0364-4
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发表时间：
2014-12-16
影响因子：
3
通讯作者：
Dickerson JA
中科院分区：
文献类型：
--
作者：
Liu R;Loraine AE;Dickerson JA
选择性剪接（Alternative Splicing，AS）作为一种转录后调控机制，是RNA-seq研究在真核生物中的重要应用。已经开发了许多软件和计算方法来检测AS。然而，大多数方法都是在动物数据上设计和测试的，例如人类和小鼠。植物的基因在许多方面不同于动物的基因，例如，平均内含子大小和首选AS类型。这些差异可能需要不同的计算方法，并提出了关于其对植物数据的有效性问题。本文的目标是对现有的计算差异剪接（或转录）检测方法进行基准测试，以便生物学家可以选择最合适的工具来实现他们的目标。本研究比较了八个流行的公共可用软件包的差异剪接分析使用模拟和真实的拟南芥RNA-seq数据。所有软件都是免费提供的。本研究探讨了不同的AS比率，阅读深度，分散模式，AS类型，样本量和注释的影响。使用真实的数据，该研究着眼于包之间的一致性，并使用PCR研究验证检测到的AS事件的子集。没有一种方法在所有情况下都是最好的。注释的准确性对选择哪种方法进行AS分析有重要影响。当AS信号相对较强且注释准确时，DEXSeq在模拟数据中表现良好。袖扣实现了更好的精确度和召回率之间的权衡，结果是最好的一个不完整的注释时提供。某些方法对于不同的AS类型执行不一致。联合收割机组合几个简单AS事件的复杂AS事件对大多数方法，尤其是对MATS，造成问题。当所有被评估的AS事件都是简单的AS事件时，MATS在真实的RNA-seq数据的分析中脱颖而出。本文的在线版本（doi：10.1186/s12859-014-0364-4）包含补充材料，可供授权用户使用。
Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals. This study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies. No single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events. The online version of this article (doi:10.1186/s12859-014-0364-4) contains supplementary material, which is available to authorized users.
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影响因子：
5.8
作者：
LeGault, Laura H.;Dewey, Colin N.
通讯作者：
Dewey, Colin N.
影响因子：
14.9
作者：
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Liu J
影响因子：
48
作者：
Katz, Yarden;Wang, Eric T.;Airoldi, Edoardo M.;Burge, Christopher B.
通讯作者：
Burge, Christopher B.
DOI：
10.1093/bioinformatics/btp472
发表时间：
2009-10-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Nicol JW;Helt GA;Blanchard SG Jr;Raja A;Loraine AE
通讯作者：
Loraine AE
影响因子：
7
作者：
Brooks, Angela N.;Yang, Li;Graveley, Brenton R.
通讯作者：
Graveley, Brenton R.